Flexibly Fair Representation Learning by Disentanglement
Authors: Elliot Creager, David Madras, Joern-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, Richard Zemel
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show empirically that the resulting encoder which does not require the sensitive attributes for inference enables the adaptation of a single representation to a variety of fair classification tasks with new target labels and subgroup definitions. We first provide proof-of-concept by generating a variant of the synthetic DSprites dataset... We then apply our method to a real-world tabular dataset (Communities & Crime) and an image dataset (Celeb-A), where we find that our method matches or exceeds the fairness-accuracy tradeoff of existing disentangled representation learning approaches on a majority of the evaluated subgroups. |
| Researcher Affiliation | Collaboration | 1University of Toronto 2Vector Institute 3University of T ubingen 4Google Research. |
| Pseudocode | No | The paper describes the methods but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | DSprites dataset4 contains 64 64-pixel images of white shapes against a black background, and was designed to evaluate whether learned representations have disentangled sources of variation. (footnote 4: https://github.com/deepmind/dsprites-dataset) Communities & Crime5 is a tabular UCI dataset containing neighborhood-level population statistics. (footnote 5: http://archive.ics.uci.edu/ml/datasets/communities+ and+crime) The Celeb A6 dataset contains over 200, 000 images of celebrity faces. (footnote 6: http://mmlab.ie.cuhk.edu.hk/projects/Celeb A.html) |
| Dataset Splits | No | The paper mentions splitting data into a 'training set' and 'audit set' and later 'test set', but does not provide specific percentages, counts, or detailed methodology for train/validation/test splits, nor does it explicitly define a validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions hyperparameters like beta and gamma, and refers to 'training details' in Appendix D (which is not provided in the given text), but does not explicitly list specific hyperparameter values (e.g., learning rate, batch size) or detailed training configurations in the main body. |